The present disclosure relates generally to process modeling, optimization, and control systems, and more particularly to methods and systems for performing model-based asset optimization, decision-making, and control for fossil-fuel fired boiler systems.
Fossil-fuel fired boiler systems have been utilized for generating electricity. One type of fossil-fuel fired boiler system combusts an air/coal mixture to generate heat energy that increases temperature of water to produce steam. The steam is utilized to drive a turbine generator that outputs electrical power. Carbon monoxide (CO) is a by-product of combusting the air/coal mixture (or any air/hydrocarbon based fuel such as a methane mixture) especially when the air to coal (fuel) ratio, also known as the air to fuel (A/F) ratio, is low. At the same time, due to the spatial variance in combustion, CO levels at particular locations in the boiler system can be greater than a predetermined CO level while other locations have CO levels less than the predetermined CO level. The variance of CO levels in the boiler system can result in increased CO emissions at an exit plane (e.g., output section) of the boiler system and ultimately at the exhaust of the boiler system through the smokestack. At the same time, Nitrogen Oxides (NOx) and other by-products of combustion need to be maintained below a predetermined level. Reducing the variance of CO levels at the exit plane of the boiler also allows for lower levels of excess oxygen (O2), NOx, and CO at the stack, thereby increasing efficiency. Typically, the average CO level at the exit plane of the boiler is highly correlated with the variance in CO at the same plane. Therefore, reducing the average planar CO has a similar intended effect as is achieved by reducing the planar CO variance. As the air to fuel ratio increases, CO decreases while NOx emissions increase. Additionally, as the quantity of intake air increases, the boiler requires more fuel to combust the larger quantity of air because the fans have to drive a larger quantity of air. As a result, the efficiency of the boiler decreases.
Current combustion optimization strategies utilize a zonal control of boilers to reduce variance of CO at the exit plane of the boiler and to allow for individualized control of burner air to fuel (A/F) ratios. Such boiler control solutions use first-principles-based modeling along with data-driven models. Data driven techniques derive relationships or transfer functions from previously gathered systems input-output data. First principles models are based on a mathematical representation of the underlying natural physical principles governing a system's input-output relationships. These models compute and adjust burner level air-flows (Primary Air and Compartment Air) and coal flows to reduce stack CO emissions using transfer functions based partially on the use of Influence Factor (IF) maps. An IF map is illustrative of a Computational Fluid Dynamics (CFD) technology based transfer function representing the effect of individual burner airflows and fuel flows at different locations in the boiler system (e.g., at an exit plane of the boiler). CFD is a first-principle based analysis technique that predicts fluid flow behavior in terms of transfer of heat, mass (such as in perspiration or dissolution), phase change (such as in freezing or boiling), chemical reaction (such as combustion), mechanical movement (such as an impeller turning), and stress or deformation of related solid structures (such as a mast bending in the wind). The information provided by the IF maps assist in controlling and minimizing the spatial average and variance of CO at the exit plane of a boiler by adjusting a particular burner's A/F ratio in such a way that provides an expected effect on a CO sensor reading located at the exit plane in the boiler system. Such a solution is presented in U.S. patent application Ser. No. 11/290,754 entitled “System, Method, And Article Of Manufacture For Adjusting CO Emission Levels At Predetermined Locations In A Boiler System,” which is incorporated by reference in its entirety as if set forth fully herein.
This method requires the creation of multiple CFD-IF maps corresponding to each unique plant operational condition. For example, a CFD-IF map corresponding to when all mills or compartments supplying coal to their respective group of burners are operational may not represent accurately a situation when one of the mills (in other words a group of burners getting coal supply from single pulverizer) may be turned off and is not operational. As a result, these CO grid mean-variance optimization algorithms have to rely on multiple IF maps for different operating conditions of a given boiler system. While such multiple CFD-IF maps can be generated, a drawback is the effort required for the generation and fine-tuning of the individual elements of each map to suit a specific boiler condition since the dimensionality of these maps is quite a challenge for standard adaptation techniques such as Kalman filter. Consequently, it has been suggested that it might be easier to fit a hyper-plane through a generic IF map and then adapt the slope and curvature of such a hyper-plane to reduce the dimensionality for adaptation. An alternative is to adapt a weighted average of multiple IF maps representing different boiler conditions such as baseload, partload, mills out of service, etc. However, simplifying the adaptation technique often results in the reduced accuracy of the adapted map in representing the condition that it's being adapted for, and hence adversely affects the optimization accuracy as well. Another drawback of the current CO grid mean-variance optimization strategy is that it does not explicitly consider higher-level boiler performance criteria such as the amount of NOx produced and the Heat Rate at a plant-level. NOx production and Heat Rate are typically mutually competing goals, i.e., a lower NOx level usually leads to a higher Heat Rate (which is coupled to lower efficiency), and vice-versa.
What is needed is an approach that addresses the above-mentioned drawbacks, thereby achieving an optimization of coal-fired boilers at both the boiler/mill level and at the burner level addressing both higher level objectives such as NOx emissions and heat rate and lower level objectives such as spatial CO variance along with stack CO reduction.